llama.cpp has released version b11054, primarily focusing on enabling TOP_K sampling operations for Qualcomm Hexagon DSPs. This addition allows more sophisticated text generation strategies on Snapdragon-based devices, which previously lacked this specific sampling method. The release also updates backend libraries to support CUDA 12.8/13.3 and ROCm 10.0, ensuring compatibility with recent GPU driver releases. Additionally, minor optimizations were applied to TOP_K row partitioning for Hexagon processors.
Read originalThis release quietly closes the hardware gap for local inference by adding native support for CUDA 13 and ROCm 10.0 across Linux and Windows builds. Users with newer NVIDIA GPUs or AMD accelerators no longer need to compile from source to get performance, as these binaries are now pre-packaged. The inclusion of OpenVINO and SYCL variants further broadens the pool of compatible hardware beyond just NVIDIA and AMD. While Apple Silicon support is present, KleidiAI has been disabled in this specific build, likely due to stability checks. This is a practical update that keeps llama.cpp relevant as hardware vendors push new driver standards.
This release quietly extends llama.cpp's hardware support to the latest NVIDIA and AMD driver stacks. By shipping native binaries for CUDA 13 and ROCm 10, it removes the friction of manual compilation for users on bleeding-edge GPUs. Apple Silicon KleidiAI is explicitly disabled here, a deliberate choice that likely prioritizes stability over new acceleration features for this specific build. The real value lies in the breadth: with Vulkan, OpenVINO, and SYCL options across Linux and Windows, it remains the most versatile local inference runtime available. Users on newer hardware can finally run pre-built binaries without hunting for custom compile flags.
This release quietly extends llama.cpp's hardware support to the latest NVIDIA and AMD stacks. By shipping native binaries for CUDA 13 and ROCm 10, it ensures compatibility with newer GPU architectures without requiring users to compile from source. The inclusion of both CUDA versions side-by-side is a pragmatic move for developers managing mixed environments. While no new model optimizations are highlighted, this update keeps the runtime relevant as hardware vendors push their latest drivers.
This release stabilizes Claude Code by patching a cascade of crashes and session hangs that plagued recent versions. The most notable functional shift is the fallback to AGENTS.md when CLAUDE.md is absent, aligning with broader industry standards for agent configuration. Gateway improvements allow better proxy handling for egress-bound environments, while numerous fixes address edge cases in file editing, plugin management, and resume functionality. It’s a maintenance-heavy update that restores reliability rather than introducing new capabilities.
Anthropic quietly fixed a cost leak in Claude Code’s auto mode. By defaulting to the server-side classifier for API and enterprise users, the update eliminates charges for classifier overhead that previously bled into session costs. This shift means developers no longer pay double for the same logic, while still retaining the ability to opt out via environment variables if needed. The change is a subtle but necessary correction to pricing transparency in automated coding workflows.
© GitHub ChangelogGitHub Copilot’s code review tool has reached general availability, shifting from experimental to a core part of the pull request workflow. The biggest leap is auto-resolution: Copilot now validates whether its own suggestions were actually fixed by subsequent commits and closes them out automatically, saving developers from manual cleanup. It also groups findings into clear states like 'Resolved' or 'Previously missed,' giving a real-time health check of the PR rather than just a static list of errors. This reduces context switching significantly, letting engineers focus on new issues while the AI handles the administrative burden of closing old ones.